Spike-in normalization¶

CHAMPAGNE supports ChIP-seq experiments that spike-in reads from alternate species. The data are normalized based on the number of reads aligning to the spike-in genome to account for differences in sequencing depth or other technical variations between samples.

Spike-in options¶

You must set the spike_genome parameter to the name of a supported genome (e.g. dmelr6.32, ecoli_k12) that is different from the genome used for the main analysis. When using spike-in normalizations, we recommend setting deeptools_normalize_using to None so that additional normalization isn't performed (see this discussion).

View the spike-in options for a full list of parameters that can be set for spike-in normalization.

normalization method¶

CHAMPAGNE implements two methods for spike-in normalization:

• guenther: This method is described in the supplementary material of 10.1016/j.celrep.2014.10.018. Reads are scaled by the minimum number of reads aligning to the spike-in genome across all samples. To use this method, set the spike_norm_method parameter to guenther.
• delorenzi: This method uses deepTools multiBamSummary --scalingFactors to calculate the scaling factors, which is similar to the method described here: 10.1101/gr.168260.113. To use this method, set the spike_norm_method parameter to delorenzi.

Examples¶

Using the guenther normalization method with D. melanogaster as the spike-in genome:

champagne run \
    --output /data/$USER/champagne_project/ \
    --genome hg38 \
    --input assets/samplesheet_full_spikein.csv \
    --spike_genome dmelr6.32 \
    --deeptools_normalize_using None \
    --spike_norm_method guenther

Using the delorenzi normalization method with E. coli as the spike-in genome:

champagne run \
    --output /data/$USER/champagne_project/ \
    --genome hg38 \
    --input assets/samplesheet_full_spikein.csv \
    --spike_genome ecoli_k12 \
    --deeptools_normalize_using None \
    --spike_norm_method delorenzi